OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience

• URL: http://arxiv.org/abs/2206.12449v1
• Date: Fri, 24 Jun 2022 18:21:26 GMT
• Title: OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience
• Authors: Miaoran Li, Baolin Peng, Jianfeng Gao, Zhu Zhang
• Abstract summary: Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks. We propose a new task, Open-Book TOD (OB-TOD), which combines TOD with QA task and expand external knowledge sources. We propose a unified model OPERA which can appropriately access explicit and implicit external knowledge to tackle the defined task.
• Score: 87.0233567695073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks. Towards the goal of constructing a conversational agent that can complete user tasks and support information seeking, it is important to build a system that handles both TOD and QA with access to various external knowledge. In this work, we propose a new task, Open-Book TOD (OB-TOD), which combines TOD with QA task and expand external knowledge sources to include both explicit knowledge sources (e.g., the Web) and implicit knowledge sources (e.g., pre-trained language models). We create a new dataset OB-MultiWOZ, where we enrich TOD sessions with QA-like information seeking experience grounded on external knowledge. We propose a unified model OPERA (Open-book End-to-end Task-oriented Dialog) which can appropriately access explicit and implicit external knowledge to tackle the defined task. Experimental results demonstrate OPERA's superior performance compared to closed-book baselines and illustrate the value of both knowledge types.

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